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Full Time Machine Learning Finance Jobs (NOW HIRING)

Hayward Hawk

Machine Learning Engineer

Manhattan, NY · Hybrid

... real estate and financial analytics. Key Responsibilities: * Model Development: Design and ... Seniority level Mid-Senior level Employment type Full-time Job function Information Technology ...

Robinhood

Machine Learning Engineer

Bellevue, WA · On-site

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be ... We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ...

Paylocity

Machine Learning Engineer

Schaumburg, IL · On-site

... your finances. And if it's career development you desire, we provide that, too! At Paylocity ... Machine Learning Engineer Position Overview Paylocity is growing its Machine Learning Engineering ...

Q2 Software, Inc.

Machine Learning Engineer

Austin, TX · On-site

Our mission is simple: build strong and diverse communities through innovative financial technology ... SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the Research and Development Team, where ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

Hang draws from years of deep expertise in loyalty, game design, and finance with employees from ... This person will implement and develop machine learning models to enhance our platform ...

Q2

Machine Learning Engineer

Austin, TX

Our mission is simple: build strong and diverse communities through innovative financial technology ... SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and ...

Booz Allen Hamilton

Machine Learning Engineer

San Diego, CA · On-site

$77K - $176K/yr

Our offerings include health, life, disability, financial, and retirement benefits, as well as paid ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

ManTech International

Machine Learning Engineer

$128K - $214K/yr

... Type Full time Description & Requirements Elevate your career with MANTECH International ... The Machine Learning Engineer will leverage their strong technical background and knowledge to ...

YouTube

Machine Learning Engineer

San Francisco, CA · On-site

Overview Direct message the job poster from YouTube This is a full-time role for a Machine Learning Engineer in YouTube for Level L3/L4. You will get a chance to work on recommendation in YouTube.

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Full Time Machine Learning Finance information

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$25K

$92.6K

$135.5K

How much do full time machine learning finance jobs pay per year?

As of Jun 18, 2026, the average yearly pay for full time machine learning finance in the United States is $92,631.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $109,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Machine Learning Finance professional, and why are they important?

To thrive as a Full Time Machine Learning Finance professional, you need a solid background in quantitative analysis, statistics, computer science, and finance, usually supported by a relevant degree. Proficiency with programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or scikit-learn), and familiarity with financial data systems are essential. Strong problem-solving abilities, attention to detail, and effective communication skills help you stand out in this field. These skills ensure the successful development and deployment of data-driven financial models that support better decision-making and risk management.

What is a Full Time Machine Learning Finance job?

A Full Time Machine Learning Finance job involves applying machine learning techniques and algorithms to financial data and problems. Professionals in this role develop predictive models for tasks such as risk assessment, trading strategies, fraud detection, and portfolio optimization. They work closely with financial analysts and data scientists to create solutions that can automate processes, improve decision-making, and identify patterns in large datasets. The role typically requires strong knowledge of both finance and advanced machine learning methods, as well as programming and data analysis skills.

What is the difference between Full Time Machine Learning Finance vs Full Time Data Scientist?

AspectFull Time Machine Learning FinanceFull Time Data Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of finance and machine learning certificationsDegree in Statistics, Computer Science, or related fields; data analysis and programming skills
Work EnvironmentFinancial institutions, hedge funds, banks, fintech companiesTech companies, consulting firms, finance, healthcare, retail
Industry UsageFinance-specific applications like risk modeling, algorithmic tradingBroad industry applications including marketing, healthcare, finance

Full Time Machine Learning Finance roles focus on applying machine learning techniques specifically to financial data and problems within financial institutions. In contrast, Full Time Data Scientist positions have a broader scope across various industries, utilizing data analysis and modeling skills to solve diverse business challenges. While both roles require strong technical skills, the finance-specific role emphasizes financial knowledge and applications.

What are some common challenges faced by machine learning professionals working in the finance sector?

Machine learning professionals in finance often encounter challenges such as dealing with sensitive and highly regulated data, ensuring model transparency and explainability for compliance purposes, and adapting to rapidly changing market conditions. Additionally, integrating machine learning models with existing financial systems and collaborating closely with domain experts, such as quantitative analysts and risk managers, are key parts of the role. Staying updated on both technological advancements and regulatory changes is also essential for success in this dynamic environment.
More about Full Time Machine Learning Finance jobs
What cities are hiring for Full Time Machine Learning Finance jobs? Cities with the most Full Time Machine Learning Finance job openings:
What are the most commonly searched types of Machine Learning Finance jobs? The most popular types of Machine Learning Finance jobs are:
What states have the most Full Time Machine Learning Finance jobs? States with the most job openings for Full Time Machine Learning Finance jobs include:
What job categories do people searching Full Time Machine Learning Finance jobs look for? The top searched job categories for Full Time Machine Learning Finance jobs are:
Full Time Machine Learning Finance jobs near you
Infographic showing various Full Time Machine Learning Finance job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $92,631 per year, or $44.5 per hour.

Machine Learning Engineer

Hayward Hawk

Manhattan, NY • Hybrid

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Get AI-powered advice on this job and more exclusive features.

Direct message the job poster from Hayward Hawk

IT Specialist | Principal Recruitment Consultant | 02895 902688 | katie@haywardhawk.com

New York – 2 days p/w

Hayward Hawk are delighted to be recruiting for a Machine Learning Engineer for their client based in Manhattan.

This is a hybrid role and will be onsite 2 days per week.

The Role:

This role is centred on developing and deploying advanced machine learning models, with a strong emphasis on time series forecasting and anomaly detection. You will work with complex, multi-source datasets, creating predictive insights that drive real estate and financial analytics.

Key Responsibilities:

  • Model Development: Design and implement predictive models to analyse trends, detect anomalies, and enhance forecasting capabilities.
  • Cloud-Based Deployment: Build scalable, cloud-native solutions leveraging Azure ML, Data Factory, and Application Insights for monitoring and optimization.
  • Stakeholder Collaboration: Engage with engineers, business teams, and domain experts to integrate AI-driven insights into real estate analytics.
  • Data Processing & Engineering: Work with large-scale structured and unstructured data, developing ETL pipelines using SQL and Python.
  • Exploratory Analysis & Feature Engineering: Perform EDA, feature selection, and model evaluation to improve predictive accuracy and interpretability.

Skills & Experience Required:

  • A minimum of 5 years of experience in Machine Learning, AI, or Data Science.
  • Proficiency in Python with expertise in ML libraries like TensorFlow, PyTorch, Scikit-learn, or XGBoost.
  • Hands-on experience in time series forecasting (e.g., ARIMA, Prophet, LSTMs).
  • Strong SQL skills for data extraction, transformation, and management.
  • Knowledge of Azure-based cloud services (Azure ML, Data Factory, Application Insights).
  • Background in data preprocessing, feature engineering, and model evaluation.
  • Ability to translate technical findings into insights that are accessible to non-technical audiences.

Would love:

  • Exposure to big data technologies such as Spark, Databricks, or Snowflake.
  • Experience in MLOps and deploying models in production environments.
  • Understanding of real estate analytics, financial modelling, or risk assessment.
  • Cloud certifications related to AI/ML on Azure or equivalent.

** Unfortunately, there is no visa sponsorship opportunity with this role**

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

Technology, Information and Media

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